Top Tech Jobs & Startup Jobs

Manages billing operations by creating client invoices, coordinating fee payments with custodians, reviewing accounts against contract terms, updating billing information, handling confidential documents, and supporting process improvements. The role collaborates with Client Service, Portfolio Management, Legal and Compliance, Audit, and other departments while ensuring accurate and timely management fee processing.

Build and operate production machine learning and generative AI infrastructure, including Azure and Databricks pipelines, CI/CD workflows, RAG systems, APIs, model-serving services, infrastructure as code, automated testing, monitoring, versioning, and governance. The role requires collaboration with AI, ML, data, infrastructure, security, and governance teams to deliver scalable, secure, reproducible, and high-performance AI solutions.

Design, develop, fine-tune, and deploy production generative AI applications using LLMs, RAG, GPU computing, Azure, Databricks, graph databases, and scalable microservices. Build FastAPI APIs, data pipelines, AI agents, and enterprise integrations. Improve model performance, reliability, explainability, observability, and compliance while supporting low-latency, high-throughput deployments and automated CI/CD and testing workflows.

Designs, develops, fine-tunes, and deploys production generative AI systems using LLMs, RAG, GPU computing, Azure, Databricks, APIs, microservices, and graph databases. Builds AI ingestion and orchestration pipelines, integrates enterprise applications, develops agent solutions, and improves model performance, reliability, explainability, security, and compliance. Requires extensive software engineering and hands-on AI experience, including distributed training, GPU optimization, model deployment, CI/CD, and automated testing.

Build and operate production machine learning and generative AI infrastructure on Azure and Databricks. Responsibilities include developing data and ML pipelines, CI/CD workflows, RAG systems, APIs, containerized model-serving services, infrastructure as code, automated testing, monitoring, artifact versioning, and operational safeguards. The role partners with ML, data, infrastructure, security, and governance teams to deliver reliable, scalable, secure, and cost-efficient AI systems.

Implements, maintains, supports, and designs information security systems and tools in cloud and SaaS environments. Coordinates security system development, testing, deployment, troubleshooting, integrations, and configuration with risk managers, application experts, technology teams, business stakeholders, and vendors. The role also supports production systems, improves security processes and documentation, and applies information security and risk management practices.

The Information Security Tools Engineer implements, maintains, supports, tests, and deploys security systems in cloud and SaaS environments. The role coordinates with risk managers, application experts, technology and business teams, and vendors; troubleshoots production security systems; contributes to system design, process maturity, and documentation; and supports enterprise security programs.

Implements, maintains, supports, tests, and deploys information security systems and tools in cloud and SaaS environments. Coordinates with risk managers, application experts, security and technology teams, business stakeholders, and vendors. Provides troubleshooting expertise, supports production security systems, contributes to system design, and improves team processes and documentation. The role requires Azure security integrations, operational technology management, enterprise project delivery, risk management, VMware knowledge, and artificial intelligence control systems experience.

Lead and mentor AI and software engineering teams delivering production-ready AI and generative AI solutions. Own architecture, experimentation, deployment, monitoring, maintenance, and continuous improvement. Establish MLOps, LLMOps, testing, observability, security, governance, and responsible AI practices. Partner with architects and business stakeholders on roadmaps and measurable outcomes while evaluating emerging AI technologies and scalable platforms.

Leads an AI engineering team delivering production-ready AI and GenAI solutions from architecture and experimentation through deployment, monitoring, and continuous improvement. Establishes MLOps, LLMOps, testing, observability, security, governance, and responsible AI practices. Partners with architects and business stakeholders on technical roadmaps, evaluates emerging technologies, and mentors engineers. Requires substantial software engineering, AI application development, cloud architecture, and engineering leadership experience, preferably in investment or wealth management.